Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.9 KiB
Average record size in memory193.3 B

Variable types

Categorical8
Text1
Numeric15

Alerts

SKU has unique values Unique
Price has unique values Unique
Revenue generated has unique values Unique
Shipping costs has unique values Unique
Manufacturing costs has unique values Unique
Defect rates has unique values Unique
Costs has unique values Unique

Reproduction

Analysis started2024-11-02 16:35:33.644984
Analysis finished2024-11-02 16:36:32.515455
Duration58.87 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

Product type
Categorical

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size928.0 B
skincare
40 
haircare
34 
cosmetics
26 

Length

Max length9
Median length8
Mean length8.26
Min length8

Characters and Unicode

Total characters826
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhaircare
2nd rowskincare
3rd rowhaircare
4th rowskincare
5th rowskincare

Common Values

ValueCountFrequency (%)
skincare 40
40.0%
haircare 34
34.0%
cosmetics 26
26.0%

Length

2024-11-02T16:36:32.739283image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-02T16:36:33.618827image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
skincare 40
40.0%
haircare 34
34.0%
cosmetics 26
26.0%

Most occurring characters

ValueCountFrequency (%)
c 126
15.3%
a 108
13.1%
r 108
13.1%
i 100
12.1%
e 100
12.1%
s 92
11.1%
k 40
 
4.8%
n 40
 
4.8%
h 34
 
4.1%
o 26
 
3.1%
Other values (2) 52
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 826
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 126
15.3%
a 108
13.1%
r 108
13.1%
i 100
12.1%
e 100
12.1%
s 92
11.1%
k 40
 
4.8%
n 40
 
4.8%
h 34
 
4.1%
o 26
 
3.1%
Other values (2) 52
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 826
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 126
15.3%
a 108
13.1%
r 108
13.1%
i 100
12.1%
e 100
12.1%
s 92
11.1%
k 40
 
4.8%
n 40
 
4.8%
h 34
 
4.1%
o 26
 
3.1%
Other values (2) 52
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 826
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 126
15.3%
a 108
13.1%
r 108
13.1%
i 100
12.1%
e 100
12.1%
s 92
11.1%
k 40
 
4.8%
n 40
 
4.8%
h 34
 
4.1%
o 26
 
3.1%
Other values (2) 52
6.3%

SKU
Text

Unique 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size928.0 B
2024-11-02T16:36:34.138056image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.9
Min length4

Characters and Unicode

Total characters490
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st rowSKU0
2nd rowSKU1
3rd rowSKU2
4th rowSKU3
5th rowSKU4
ValueCountFrequency (%)
sku0 1
 
1.0%
sku12 1
 
1.0%
sku2 1
 
1.0%
sku3 1
 
1.0%
sku4 1
 
1.0%
sku5 1
 
1.0%
sku6 1
 
1.0%
sku7 1
 
1.0%
sku8 1
 
1.0%
sku9 1
 
1.0%
Other values (90) 90
90.0%
2024-11-02T16:36:35.293513image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 100
20.4%
K 100
20.4%
U 100
20.4%
6 20
 
4.1%
3 20
 
4.1%
7 20
 
4.1%
2 20
 
4.1%
1 20
 
4.1%
9 20
 
4.1%
8 20
 
4.1%
Other values (3) 50
10.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 490
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 100
20.4%
K 100
20.4%
U 100
20.4%
6 20
 
4.1%
3 20
 
4.1%
7 20
 
4.1%
2 20
 
4.1%
1 20
 
4.1%
9 20
 
4.1%
8 20
 
4.1%
Other values (3) 50
10.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 490
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 100
20.4%
K 100
20.4%
U 100
20.4%
6 20
 
4.1%
3 20
 
4.1%
7 20
 
4.1%
2 20
 
4.1%
1 20
 
4.1%
9 20
 
4.1%
8 20
 
4.1%
Other values (3) 50
10.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 490
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 100
20.4%
K 100
20.4%
U 100
20.4%
6 20
 
4.1%
3 20
 
4.1%
7 20
 
4.1%
2 20
 
4.1%
1 20
 
4.1%
9 20
 
4.1%
8 20
 
4.1%
Other values (3) 50
10.2%

Price
Real number (ℝ)

Unique 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.462461
Minimum1.699976
Maximum99.171329
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2024-11-02T16:36:35.836815image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1.699976
5-th percentile4.0507218
Q119.597823
median51.239831
Q377.198228
95-th percentile96.396366
Maximum99.171329
Range97.471353
Interquartile range (IQR)57.600405

Descriptive statistics

Standard deviation31.168193
Coefficient of variation (CV)0.63013833
Kurtosis-1.3734706
Mean49.462461
Median Absolute Deviation (MAD)28.292667
Skewness-0.022538919
Sum4946.2461
Variance971.45624
MonotonicityNot monotonic
2024-11-02T16:36:36.407088image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69.80800554 1
 
1.0%
13.01737679 1
 
1.0%
83.85101768 1
 
1.0%
90.20442752 1
 
1.0%
6.381533163 1
 
1.0%
47.91454182 1
 
1.0%
54.86552852 1
 
1.0%
37.93181238 1
 
1.0%
87.75543235 1
 
1.0%
26.03486977 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1.699976014 1
1.0%
2.397274706 1
1.0%
3.037688725 1
1.0%
3.170011414 1
1.0%
3.526111259 1
1.0%
4.078332863 1
1.0%
4.156308359 1
1.0%
4.324341186 1
1.0%
4.805496036 1
1.0%
6.306883176 1
1.0%
ValueCountFrequency (%)
99.17132864 1
1.0%
99.11329162 1
1.0%
98.03182966 1
1.0%
97.76008558 1
1.0%
97.44694662 1
1.0%
96.34107244 1
1.0%
95.71213588 1
1.0%
92.99688423 1
1.0%
92.55736081 1
1.0%
91.12831835 1
1.0%

Availability
Real number (ℝ)

Distinct63
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.4
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2024-11-02T16:36:36.994680image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q122.75
median43.5
Q375
95-th percentile96.05
Maximum100
Range99
Interquartile range (IQR)52.25

Descriptive statistics

Standard deviation30.743317
Coefficient of variation (CV)0.63519249
Kurtosis-1.3319932
Mean48.4
Median Absolute Deviation (MAD)27.5
Skewness0.18361821
Sum4840
Variance945.15152
MonotonicityNot monotonic
2024-11-02T16:36:37.622104image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 4
 
4.0%
55 3
 
3.0%
75 3
 
3.0%
29 3
 
3.0%
16 3
 
3.0%
56 3
 
3.0%
23 3
 
3.0%
90 3
 
3.0%
26 3
 
3.0%
34 3
 
3.0%
Other values (53) 69
69.0%
ValueCountFrequency (%)
1 2
2.0%
3 1
 
1.0%
5 2
2.0%
6 1
 
1.0%
9 2
2.0%
10 2
2.0%
11 4
4.0%
12 1
 
1.0%
13 1
 
1.0%
14 2
2.0%
ValueCountFrequency (%)
100 1
 
1.0%
99 1
 
1.0%
98 1
 
1.0%
97 2
2.0%
96 1
 
1.0%
95 2
2.0%
94 1
 
1.0%
93 2
2.0%
91 1
 
1.0%
90 3
3.0%

Number of products sold
Real number (ℝ)

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean460.99
Minimum8
Maximum996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2024-11-02T16:36:38.199466image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile60.5
Q1184.25
median392.5
Q3704.25
95-th percentile960.15
Maximum996
Range988
Interquartile range (IQR)520

Descriptive statistics

Standard deviation303.78007
Coefficient of variation (CV)0.65897324
Kurtosis-1.2513936
Mean460.99
Median Absolute Deviation (MAD)241.5
Skewness0.28141802
Sum46099
Variance92282.333
MonotonicityNot monotonic
2024-11-02T16:36:38.682389image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
336 2
 
2.0%
320 2
 
2.0%
134 2
 
2.0%
963 2
 
2.0%
246 1
 
1.0%
637 1
 
1.0%
32 1
 
1.0%
511 1
 
1.0%
163 1
 
1.0%
513 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
8 1
1.0%
24 1
1.0%
25 1
1.0%
29 1
1.0%
32 1
1.0%
62 1
1.0%
65 1
1.0%
79 1
1.0%
83 1
1.0%
93 1
1.0%
ValueCountFrequency (%)
996 1
1.0%
987 1
1.0%
980 1
1.0%
963 2
2.0%
960 1
1.0%
946 1
1.0%
933 1
1.0%
919 1
1.0%
916 1
1.0%
913 1
1.0%

Revenue generated
Real number (ℝ)

Unique 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5776.0482
Minimum1061.6185
Maximum9866.4655
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2024-11-02T16:36:39.268822image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1061.6185
5-th percentile1835.248
Q12812.8472
median6006.352
Q38253.9769
95-th percentile9571.8604
Maximum9866.4655
Range8804.8469
Interquartile range (IQR)5441.1298

Descriptive statistics

Standard deviation2732.8417
Coefficient of variation (CV)0.47313347
Kurtosis-1.4175921
Mean5776.0482
Median Absolute Deviation (MAD)2472.9427
Skewness-0.17382939
Sum577604.82
Variance7468424
MonotonicityNot monotonic
2024-11-02T16:36:39.777338image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8661.996792 1
 
1.0%
4256.949141 1
 
1.0%
7910.886916 1
 
1.0%
2633.121981 1
 
1.0%
8180.337085 1
 
1.0%
7014.887987 1
 
1.0%
1752.381087 1
 
1.0%
3550.218433 1
 
1.0%
9473.798033 1
 
1.0%
8367.721618 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1061.618523 1
1.0%
1229.591029 1
1.0%
1292.458418 1
1.0%
1605.8669 1
1.0%
1752.381087 1
1.0%
1839.609426 1
1.0%
1889.07359 1
1.0%
1912.465663 1
1.0%
1935.206794 1
1.0%
2021.14981 1
1.0%
ValueCountFrequency (%)
9866.465458 1
1.0%
9692.31804 1
1.0%
9655.135103 1
1.0%
9592.63357 1
1.0%
9577.749626 1
1.0%
9571.550487 1
1.0%
9473.798033 1
1.0%
9444.742033 1
1.0%
9435.762609 1
1.0%
9364.673505 1
1.0%
Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size928.0 B
Unknown
31 
Female
25 
Non-binary
23 
Male
21 

Length

Max length10
Median length7
Mean length6.81
Min length4

Characters and Unicode

Total characters681
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNon-binary
2nd rowFemale
3rd rowUnknown
4th rowNon-binary
5th rowNon-binary

Common Values

ValueCountFrequency (%)
Unknown 31
31.0%
Female 25
25.0%
Non-binary 23
23.0%
Male 21
21.0%

Length

2024-11-02T16:36:40.117112image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-02T16:36:40.439011image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
unknown 31
31.0%
female 25
25.0%
non-binary 23
23.0%
male 21
21.0%

Most occurring characters

ValueCountFrequency (%)
n 139
20.4%
e 71
10.4%
a 69
10.1%
o 54
 
7.9%
l 46
 
6.8%
U 31
 
4.6%
w 31
 
4.6%
k 31
 
4.6%
F 25
 
3.7%
m 25
 
3.7%
Other values (7) 159
23.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 681
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 139
20.4%
e 71
10.4%
a 69
10.1%
o 54
 
7.9%
l 46
 
6.8%
U 31
 
4.6%
w 31
 
4.6%
k 31
 
4.6%
F 25
 
3.7%
m 25
 
3.7%
Other values (7) 159
23.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 681
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 139
20.4%
e 71
10.4%
a 69
10.1%
o 54
 
7.9%
l 46
 
6.8%
U 31
 
4.6%
w 31
 
4.6%
k 31
 
4.6%
F 25
 
3.7%
m 25
 
3.7%
Other values (7) 159
23.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 681
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 139
20.4%
e 71
10.4%
a 69
10.1%
o 54
 
7.9%
l 46
 
6.8%
U 31
 
4.6%
w 31
 
4.6%
k 31
 
4.6%
F 25
 
3.7%
m 25
 
3.7%
Other values (7) 159
23.3%

Stock levels
Real number (ℝ)

Distinct65
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.77
Minimum0
Maximum100
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2024-11-02T16:36:40.878786image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q116.75
median47.5
Q373
95-th percentile97
Maximum100
Range100
Interquartile range (IQR)56.25

Descriptive statistics

Standard deviation31.369372
Coefficient of variation (CV)0.65667514
Kurtosis-1.2343435
Mean47.77
Median Absolute Deviation (MAD)29
Skewness0.10159282
Sum4777
Variance984.03747
MonotonicityNot monotonic
2024-11-02T16:36:41.522522image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 5
 
5.0%
90 4
 
4.0%
48 3
 
3.0%
100 3
 
3.0%
10 3
 
3.0%
4 3
 
3.0%
96 2
 
2.0%
60 2
 
2.0%
42 2
 
2.0%
57 2
 
2.0%
Other values (55) 71
71.0%
ValueCountFrequency (%)
0 1
 
1.0%
1 2
 
2.0%
2 1
 
1.0%
4 3
3.0%
5 5
5.0%
6 1
 
1.0%
9 1
 
1.0%
10 3
3.0%
11 1
 
1.0%
12 1
 
1.0%
ValueCountFrequency (%)
100 3
3.0%
98 1
 
1.0%
97 2
2.0%
96 2
2.0%
95 1
 
1.0%
93 2
2.0%
92 1
 
1.0%
90 4
4.0%
89 1
 
1.0%
86 1
 
1.0%

Lead times
Real number (ℝ)

Distinct29
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.96
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2024-11-02T16:36:41.998868image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q18
median17
Q324
95-th percentile29
Maximum30
Range29
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.7858012
Coefficient of variation (CV)0.5504888
Kurtosis-1.1888488
Mean15.96
Median Absolute Deviation (MAD)8
Skewness-0.12983854
Sum1596
Variance77.190303
MonotonicityNot monotonic
2024-11-02T16:36:42.515722image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 6
 
6.0%
27 6
 
6.0%
17 6
 
6.0%
25 5
 
5.0%
19 5
 
5.0%
23 5
 
5.0%
8 5
 
5.0%
29 5
 
5.0%
26 5
 
5.0%
18 4
 
4.0%
Other values (19) 48
48.0%
ValueCountFrequency (%)
1 6
6.0%
2 3
3.0%
3 1
 
1.0%
4 2
 
2.0%
5 4
4.0%
6 2
 
2.0%
7 3
3.0%
8 5
5.0%
9 2
 
2.0%
10 3
3.0%
ValueCountFrequency (%)
30 2
 
2.0%
29 5
5.0%
28 1
 
1.0%
27 6
6.0%
26 5
5.0%
25 5
5.0%
24 3
3.0%
23 5
5.0%
22 2
 
2.0%
20 2
 
2.0%

Order quantities
Real number (ℝ)

Distinct61
Distinct (%)61.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.22
Minimum1
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2024-11-02T16:36:43.093004image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q126
median52
Q371.25
95-th percentile88
Maximum96
Range95
Interquartile range (IQR)45.25

Descriptive statistics

Standard deviation26.784429
Coefficient of variation (CV)0.54417776
Kurtosis-1.1192733
Mean49.22
Median Absolute Deviation (MAD)23.5
Skewness-0.10737313
Sum4922
Variance717.40566
MonotonicityNot monotonic
2024-11-02T16:36:43.640749image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85 6
 
6.0%
72 4
 
4.0%
66 4
 
4.0%
51 3
 
3.0%
26 3
 
3.0%
11 3
 
3.0%
52 3
 
3.0%
27 3
 
3.0%
22 3
 
3.0%
96 2
 
2.0%
Other values (51) 66
66.0%
ValueCountFrequency (%)
1 1
 
1.0%
2 1
 
1.0%
4 1
 
1.0%
6 1
 
1.0%
7 2
2.0%
8 1
 
1.0%
9 2
2.0%
10 2
2.0%
11 3
3.0%
15 1
 
1.0%
ValueCountFrequency (%)
96 2
 
2.0%
95 1
 
1.0%
94 1
 
1.0%
88 2
 
2.0%
85 6
6.0%
83 2
 
2.0%
82 1
 
1.0%
80 2
 
2.0%
78 2
 
2.0%
77 1
 
1.0%

Shipping times
Real number (ℝ)

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.75
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2024-11-02T16:36:44.152117image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13.75
median6
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4.25

Descriptive statistics

Standard deviation2.7242829
Coefficient of variation (CV)0.47378833
Kurtosis-1.0712955
Mean5.75
Median Absolute Deviation (MAD)2
Skewness-0.2815893
Sum575
Variance7.4217172
MonotonicityNot monotonic
2024-11-02T16:36:44.647768image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
8 16
16.0%
7 14
14.0%
9 11
11.0%
4 10
10.0%
6 10
10.0%
3 10
10.0%
1 10
10.0%
5 8
8.0%
10 6
 
6.0%
2 5
 
5.0%
ValueCountFrequency (%)
1 10
10.0%
2 5
 
5.0%
3 10
10.0%
4 10
10.0%
5 8
8.0%
6 10
10.0%
7 14
14.0%
8 16
16.0%
9 11
11.0%
10 6
 
6.0%
ValueCountFrequency (%)
10 6
 
6.0%
9 11
11.0%
8 16
16.0%
7 14
14.0%
6 10
10.0%
5 8
8.0%
4 10
10.0%
3 10
10.0%
2 5
 
5.0%
1 10
10.0%
Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size928.0 B
Carrier B
43 
Carrier C
29 
Carrier A
28 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters900
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCarrier B
2nd rowCarrier A
3rd rowCarrier B
4th rowCarrier C
5th rowCarrier A

Common Values

ValueCountFrequency (%)
Carrier B 43
43.0%
Carrier C 29
29.0%
Carrier A 28
28.0%

Length

2024-11-02T16:36:45.122265image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-02T16:36:45.456744image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
carrier 100
50.0%
b 43
21.5%
c 29
 
14.5%
a 28
 
14.0%

Most occurring characters

ValueCountFrequency (%)
r 300
33.3%
C 129
14.3%
a 100
 
11.1%
i 100
 
11.1%
e 100
 
11.1%
100
 
11.1%
B 43
 
4.8%
A 28
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 900
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 300
33.3%
C 129
14.3%
a 100
 
11.1%
i 100
 
11.1%
e 100
 
11.1%
100
 
11.1%
B 43
 
4.8%
A 28
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 900
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 300
33.3%
C 129
14.3%
a 100
 
11.1%
i 100
 
11.1%
e 100
 
11.1%
100
 
11.1%
B 43
 
4.8%
A 28
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 900
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 300
33.3%
C 129
14.3%
a 100
 
11.1%
i 100
 
11.1%
e 100
 
11.1%
100
 
11.1%
B 43
 
4.8%
A 28
 
3.1%

Shipping costs
Real number (ℝ)

Unique 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5481491
Minimum1.0134866
Maximum9.9298162
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2024-11-02T16:36:45.741229image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1.0134866
5-th percentile1.4055747
Q13.5402477
median5.320534
Q37.6016949
95-th percentile9.5745308
Maximum9.9298162
Range8.9163297
Interquartile range (IQR)4.0614472

Descriptive statistics

Standard deviation2.6513755
Coefficient of variation (CV)0.47788469
Kurtosis-1.1835666
Mean5.5481491
Median Absolute Deviation (MAD)2.231315
Skewness-0.053738287
Sum554.81491
Variance7.0297922
MonotonicityNot monotonic
2024-11-02T16:36:46.064827image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.956572139 1
 
1.0%
2.457933528 1
 
1.0%
1.512936837 1
 
1.0%
6.59961416 1
 
1.0%
9.228190317 1
 
1.0%
6.315717755 1
 
1.0%
9.70528679 1
 
1.0%
1.194251865 1
 
1.0%
9.147811545 1
 
1.0%
2.216142729 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1.013486566 1
1.0%
1.019487571 1
1.0%
1.194251865 1
1.0%
1.311023756 1
1.0%
1.32527401 1
1.0%
1.409801095 1
1.0%
1.45430531 1
1.0%
1.512936837 1
1.0%
1.532655274 1
1.0%
1.729568564 1
1.0%
ValueCountFrequency (%)
9.929816245 1
1.0%
9.898140508 1
1.0%
9.741291689 1
1.0%
9.716574771 1
1.0%
9.70528679 1
1.0%
9.567648921 1
1.0%
9.537283061 1
1.0%
9.235931437 1
1.0%
9.228190317 1
1.0%
9.160558535 1
1.0%

Supplier name
Categorical

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size928.0 B
Supplier 1
27 
Supplier 2
22 
Supplier 5
18 
Supplier 4
18 
Supplier 3
15 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1000
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSupplier 3
2nd rowSupplier 3
3rd rowSupplier 1
4th rowSupplier 5
5th rowSupplier 1

Common Values

ValueCountFrequency (%)
Supplier 1 27
27.0%
Supplier 2 22
22.0%
Supplier 5 18
18.0%
Supplier 4 18
18.0%
Supplier 3 15
15.0%

Length

2024-11-02T16:36:46.351788image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-02T16:36:46.626287image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
supplier 100
50.0%
1 27
 
13.5%
2 22
 
11.0%
5 18
 
9.0%
4 18
 
9.0%
3 15
 
7.5%

Most occurring characters

ValueCountFrequency (%)
p 200
20.0%
S 100
10.0%
u 100
10.0%
l 100
10.0%
i 100
10.0%
e 100
10.0%
r 100
10.0%
100
10.0%
1 27
 
2.7%
2 22
 
2.2%
Other values (3) 51
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 200
20.0%
S 100
10.0%
u 100
10.0%
l 100
10.0%
i 100
10.0%
e 100
10.0%
r 100
10.0%
100
10.0%
1 27
 
2.7%
2 22
 
2.2%
Other values (3) 51
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 200
20.0%
S 100
10.0%
u 100
10.0%
l 100
10.0%
i 100
10.0%
e 100
10.0%
r 100
10.0%
100
10.0%
1 27
 
2.7%
2 22
 
2.2%
Other values (3) 51
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 200
20.0%
S 100
10.0%
u 100
10.0%
l 100
10.0%
i 100
10.0%
e 100
10.0%
r 100
10.0%
100
10.0%
1 27
 
2.7%
2 22
 
2.2%
Other values (3) 51
 
5.1%

Location
Categorical

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size928.0 B
Kolkata
25 
Mumbai
22 
Chennai
20 
Bangalore
18 
Delhi
15 

Length

Max length9
Median length7
Mean length6.84
Min length5

Characters and Unicode

Total characters684
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMumbai
2nd rowMumbai
3rd rowMumbai
4th rowKolkata
5th rowDelhi

Common Values

ValueCountFrequency (%)
Kolkata 25
25.0%
Mumbai 22
22.0%
Chennai 20
20.0%
Bangalore 18
18.0%
Delhi 15
15.0%

Length

2024-11-02T16:36:46.927903image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-02T16:36:47.193123image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
kolkata 25
25.0%
mumbai 22
22.0%
chennai 20
20.0%
bangalore 18
18.0%
delhi 15
15.0%

Most occurring characters

ValueCountFrequency (%)
a 128
18.7%
l 58
 
8.5%
n 58
 
8.5%
i 57
 
8.3%
e 53
 
7.7%
o 43
 
6.3%
h 35
 
5.1%
K 25
 
3.7%
k 25
 
3.7%
t 25
 
3.7%
Other values (9) 177
25.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 684
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 128
18.7%
l 58
 
8.5%
n 58
 
8.5%
i 57
 
8.3%
e 53
 
7.7%
o 43
 
6.3%
h 35
 
5.1%
K 25
 
3.7%
k 25
 
3.7%
t 25
 
3.7%
Other values (9) 177
25.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 684
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 128
18.7%
l 58
 
8.5%
n 58
 
8.5%
i 57
 
8.3%
e 53
 
7.7%
o 43
 
6.3%
h 35
 
5.1%
K 25
 
3.7%
k 25
 
3.7%
t 25
 
3.7%
Other values (9) 177
25.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 684
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 128
18.7%
l 58
 
8.5%
n 58
 
8.5%
i 57
 
8.3%
e 53
 
7.7%
o 43
 
6.3%
h 35
 
5.1%
K 25
 
3.7%
k 25
 
3.7%
t 25
 
3.7%
Other values (9) 177
25.9%

Lead time
Real number (ℝ)

Distinct29
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.08
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2024-11-02T16:36:47.452153image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q110
median18
Q325
95-th percentile29
Maximum30
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8462513
Coefficient of variation (CV)0.5179304
Kurtosis-1.1745173
Mean17.08
Median Absolute Deviation (MAD)7.5
Skewness-0.32620585
Sum1708
Variance78.256162
MonotonicityNot monotonic
2024-11-02T16:36:47.743629image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
18 9
 
9.0%
24 6
 
6.0%
10 6
 
6.0%
28 6
 
6.0%
25 6
 
6.0%
29 5
 
5.0%
26 5
 
5.0%
21 4
 
4.0%
4 4
 
4.0%
1 4
 
4.0%
Other values (19) 45
45.0%
ValueCountFrequency (%)
1 4
4.0%
2 2
 
2.0%
3 3
3.0%
4 4
4.0%
5 3
3.0%
6 1
 
1.0%
7 2
 
2.0%
8 2
 
2.0%
9 2
 
2.0%
10 6
6.0%
ValueCountFrequency (%)
30 2
 
2.0%
29 5
5.0%
28 6
6.0%
27 3
3.0%
26 5
5.0%
25 6
6.0%
24 6
6.0%
23 3
3.0%
22 3
3.0%
21 4
4.0%

Production volumes
Real number (ℝ)

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean567.84
Minimum104
Maximum985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2024-11-02T16:36:48.053003image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum104
5-th percentile172.9
Q1352
median568.5
Q3797
95-th percentile953.1
Maximum985
Range881
Interquartile range (IQR)445

Descriptive statistics

Standard deviation263.04686
Coefficient of variation (CV)0.46324116
Kurtosis-1.2932782
Mean567.84
Median Absolute Deviation (MAD)230
Skewness-0.076547131
Sum56784
Variance69193.651
MonotonicityNot monotonic
2024-11-02T16:36:48.371743image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
791 2
 
2.0%
671 2
 
2.0%
867 2
 
2.0%
171 2
 
2.0%
215 1
 
1.0%
358 1
 
1.0%
444 1
 
1.0%
152 1
 
1.0%
258 1
 
1.0%
775 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
104 1
1.0%
109 1
1.0%
152 1
1.0%
171 2
2.0%
173 1
1.0%
176 1
1.0%
177 1
1.0%
179 1
1.0%
198 1
1.0%
202 1
1.0%
ValueCountFrequency (%)
985 1
1.0%
971 1
1.0%
964 1
1.0%
963 1
1.0%
955 1
1.0%
953 1
1.0%
937 1
1.0%
934 1
1.0%
929 1
1.0%
921 1
1.0%

Manufacturing lead time
Real number (ℝ)

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.77
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2024-11-02T16:36:48.677615image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median14
Q323
95-th percentile29
Maximum30
Range29
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.9124303
Coefficient of variation (CV)0.60341437
Kurtosis-1.2944597
Mean14.77
Median Absolute Deviation (MAD)7.5
Skewness0.18499721
Sum1477
Variance79.431414
MonotonicityNot monotonic
2024-11-02T16:36:48.973595image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
7 8
 
8.0%
28 7
 
7.0%
11 6
 
6.0%
23 5
 
5.0%
18 5
 
5.0%
4 5
 
5.0%
5 5
 
5.0%
29 4
 
4.0%
10 4
 
4.0%
21 4
 
4.0%
Other values (20) 47
47.0%
ValueCountFrequency (%)
1 3
 
3.0%
2 3
 
3.0%
3 3
 
3.0%
4 5
5.0%
5 5
5.0%
6 3
 
3.0%
7 8
8.0%
8 3
 
3.0%
9 2
 
2.0%
10 4
4.0%
ValueCountFrequency (%)
30 2
 
2.0%
29 4
4.0%
28 7
7.0%
27 2
 
2.0%
26 2
 
2.0%
25 2
 
2.0%
24 3
3.0%
23 5
5.0%
22 1
 
1.0%
21 4
4.0%

Manufacturing costs
Real number (ℝ)

Unique 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.266693
Minimum1.0850686
Maximum99.466109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2024-11-02T16:36:49.288664image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1.0850686
5-th percentile5.7820993
Q122.983299
median45.905622
Q368.621026
95-th percentile97.113967
Maximum99.466109
Range98.38104
Interquartile range (IQR)45.637726

Descriptive statistics

Standard deviation28.982841
Coefficient of variation (CV)0.61317683
Kurtosis-1.0923693
Mean47.266693
Median Absolute Deviation (MAD)23.065387
Skewness0.19149769
Sum4726.6693
Variance840.00509
MonotonicityNot monotonic
2024-11-02T16:36:49.649492image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46.27987924 1
 
1.0%
20.07500398 1
 
1.0%
46.8702388 1
 
1.0%
55.7604929 1
 
1.0%
30.66167748 1
 
1.0%
11.44078182 1
 
1.0%
77.62776581 1
 
1.0%
97.11358156 1
 
1.0%
7.057876147 1
 
1.0%
42.08443674 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1.08506857 1
1.0%
1.597222743 1
1.0%
1.900762244 1
1.0%
4.465278435 1
1.0%
5.604690864 1
1.0%
5.79143663 1
1.0%
5.930693646 1
1.0%
7.057876147 1
1.0%
8.693042426 1
1.0%
9.005807429 1
1.0%
ValueCountFrequency (%)
99.4661086 1
1.0%
98.60995724 1
1.0%
97.82905011 1
1.0%
97.7305938 1
1.0%
97.12128175 1
1.0%
97.11358156 1
1.0%
96.52735279 1
1.0%
96.42282064 1
1.0%
95.33206455 1
1.0%
92.0651606 1
1.0%
Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size928.0 B
Pending
41 
Fail
36 
Pass
23 

Length

Max length7
Median length4
Mean length5.23
Min length4

Characters and Unicode

Total characters523
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPending
2nd rowPending
3rd rowPending
4th rowFail
5th rowFail

Common Values

ValueCountFrequency (%)
Pending 41
41.0%
Fail 36
36.0%
Pass 23
23.0%

Length

2024-11-02T16:36:49.966835image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-02T16:36:50.199077image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
pending 41
41.0%
fail 36
36.0%
pass 23
23.0%

Most occurring characters

ValueCountFrequency (%)
n 82
15.7%
i 77
14.7%
P 64
12.2%
a 59
11.3%
s 46
8.8%
e 41
7.8%
d 41
7.8%
g 41
7.8%
F 36
6.9%
l 36
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 523
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 82
15.7%
i 77
14.7%
P 64
12.2%
a 59
11.3%
s 46
8.8%
e 41
7.8%
d 41
7.8%
g 41
7.8%
F 36
6.9%
l 36
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 523
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 82
15.7%
i 77
14.7%
P 64
12.2%
a 59
11.3%
s 46
8.8%
e 41
7.8%
d 41
7.8%
g 41
7.8%
F 36
6.9%
l 36
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 523
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 82
15.7%
i 77
14.7%
P 64
12.2%
a 59
11.3%
s 46
8.8%
e 41
7.8%
d 41
7.8%
g 41
7.8%
F 36
6.9%
l 36
6.9%

Defect rates
Real number (ℝ)

Unique 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.277158
Minimum0.018607568
Maximum4.9392553
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2024-11-02T16:36:50.511581image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.018607568
5-th percentile0.13045871
Q11.00965
median2.1418627
Q33.5639953
95-th percentile4.7470562
Maximum4.9392553
Range4.9206477
Interquartile range (IQR)2.5543454

Descriptive statistics

Standard deviation1.4613655
Coefficient of variation (CV)0.64174974
Kurtosis-1.1139982
Mean2.277158
Median Absolute Deviation (MAD)1.2702879
Skewness0.12964432
Sum227.7158
Variance2.1355893
MonotonicityNot monotonic
2024-11-02T16:36:50.877331image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2264103608 1
 
1.0%
3.63284329 1
 
1.0%
4.620546065 1
 
1.0%
3.213329607 1
 
1.0%
2.078750608 1
 
1.0%
1.830575599 1
 
1.0%
1.362387989 1
 
1.0%
1.983467872 1
 
1.0%
0.1319554443 1
 
1.0%
3.448063288 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
0.01860756763 1
1.0%
0.02116982137 1
1.0%
0.0453022624 1
1.0%
0.1006828516 1
1.0%
0.1020207549 1
1.0%
0.1319554443 1
1.0%
0.1594863147 1
1.0%
0.1658716275 1
1.0%
0.2264103608 1
1.0%
0.3334318252 1
1.0%
ValueCountFrequency (%)
4.939255289 1
1.0%
4.911095955 1
1.0%
4.854068026 1
1.0%
4.843456577 1
1.0%
4.754800805 1
1.0%
4.746648621 1
1.0%
4.620546065 1
1.0%
4.580592619 1
1.0%
4.548919659 1
1.0%
4.367470538 1
1.0%
Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size928.0 B
Road
29 
Rail
28 
Air
26 
Sea
17 

Length

Max length4
Median length4
Mean length3.57
Min length3

Characters and Unicode

Total characters357
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRoad
2nd rowRoad
3rd rowAir
4th rowRail
5th rowAir

Common Values

ValueCountFrequency (%)
Road 29
29.0%
Rail 28
28.0%
Air 26
26.0%
Sea 17
17.0%

Length

2024-11-02T16:36:51.184934image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-02T16:36:51.422168image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
road 29
29.0%
rail 28
28.0%
air 26
26.0%
sea 17
17.0%

Most occurring characters

ValueCountFrequency (%)
a 74
20.7%
R 57
16.0%
i 54
15.1%
o 29
 
8.1%
d 29
 
8.1%
l 28
 
7.8%
A 26
 
7.3%
r 26
 
7.3%
S 17
 
4.8%
e 17
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 357
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 74
20.7%
R 57
16.0%
i 54
15.1%
o 29
 
8.1%
d 29
 
8.1%
l 28
 
7.8%
A 26
 
7.3%
r 26
 
7.3%
S 17
 
4.8%
e 17
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 357
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 74
20.7%
R 57
16.0%
i 54
15.1%
o 29
 
8.1%
d 29
 
8.1%
l 28
 
7.8%
A 26
 
7.3%
r 26
 
7.3%
S 17
 
4.8%
e 17
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 357
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 74
20.7%
R 57
16.0%
i 54
15.1%
o 29
 
8.1%
d 29
 
8.1%
l 28
 
7.8%
A 26
 
7.3%
r 26
 
7.3%
S 17
 
4.8%
e 17
 
4.8%

Routes
Categorical

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size928.0 B
Route A
43 
Route B
37 
Route C
20 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters700
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRoute B
2nd rowRoute B
3rd rowRoute C
4th rowRoute A
5th rowRoute A

Common Values

ValueCountFrequency (%)
Route A 43
43.0%
Route B 37
37.0%
Route C 20
20.0%

Length

2024-11-02T16:36:51.684964image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-02T16:36:51.924560image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
route 100
50.0%
a 43
21.5%
b 37
 
18.5%
c 20
 
10.0%

Most occurring characters

ValueCountFrequency (%)
R 100
14.3%
o 100
14.3%
u 100
14.3%
t 100
14.3%
e 100
14.3%
100
14.3%
A 43
6.1%
B 37
 
5.3%
C 20
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 100
14.3%
o 100
14.3%
u 100
14.3%
t 100
14.3%
e 100
14.3%
100
14.3%
A 43
6.1%
B 37
 
5.3%
C 20
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 100
14.3%
o 100
14.3%
u 100
14.3%
t 100
14.3%
e 100
14.3%
100
14.3%
A 43
6.1%
B 37
 
5.3%
C 20
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 100
14.3%
o 100
14.3%
u 100
14.3%
t 100
14.3%
e 100
14.3%
100
14.3%
A 43
6.1%
B 37
 
5.3%
C 20
 
2.9%

Costs
Real number (ℝ)

Unique 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean529.24578
Minimum103.91625
Maximum997.41345
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2024-11-02T16:36:52.207762image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum103.91625
5-th percentile134.04373
Q1318.77846
median520.43044
Q3763.07823
95-th percentile923.73036
Maximum997.41345
Range893.4972
Interquartile range (IQR)444.29978

Descriptive statistics

Standard deviation258.3017
Coefficient of variation (CV)0.48805622
Kurtosis-1.1693231
Mean529.24578
Median Absolute Deviation (MAD)239.5189
Skewness0.040144408
Sum52924.578
Variance66719.766
MonotonicityNot monotonic
2024-11-02T16:36:52.531595image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
187.7520755 1
 
1.0%
687.2861779 1
 
1.0%
866.4728001 1
 
1.0%
677.9445698 1
 
1.0%
405.1670679 1
 
1.0%
183.2728987 1
 
1.0%
207.6632062 1
 
1.0%
299.7063031 1
 
1.0%
169.2718014 1
 
1.0%
393.8433486 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
103.916248 1
1.0%
110.3643352 1
1.0%
123.4370275 1
1.0%
126.7230334 1
1.0%
127.8618 1
1.0%
134.3690969 1
1.0%
141.9202818 1
1.0%
164.3665282 1
1.0%
169.2718014 1
1.0%
183.2728987 1
1.0%
ValueCountFrequency (%)
997.4134501 1
1.0%
996.778315 1
1.0%
995.9294615 1
1.0%
990.0784725 1
1.0%
929.23529 1
1.0%
923.4406317 1
1.0%
882.1988635 1
1.0%
880.0809882 1
1.0%
879.3592177 1
1.0%
873.129648 1
1.0%

Interactions

2024-11-02T16:36:27.452349image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:35.719121image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:39.122778image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:44.048290image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:47.115557image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:50.574494image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:53.596764image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:58.138752image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:01.972507image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:05.057073image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:08.135822image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:12.206954image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:16.603142image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:19.802175image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:23.218338image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:27.772162image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:35.926625image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:39.432623image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:44.274562image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:47.307991image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:50.760468image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:53.821096image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:58.477411image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:02.189807image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:05.276879image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:08.334853image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:12.499574image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:16.803270image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:20.008663image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:23.503236image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:28.042077image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:36.109246image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:39.701644image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:44.492867image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:47.570832image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:50.954364image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:54.024361image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:58.794135image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:02.372814image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:05.488910image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:08.561011image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:12.803283image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:17.025452image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:20.239294image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:23.811110image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:28.309648image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:36.298260image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:40.028035image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:44.671786image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:47.762583image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:51.152821image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:54.297600image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:59.086084image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:02.622109image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:05.684709image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:08.786892image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:13.159357image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:17.229144image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:20.465334image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:24.009161image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:28.629608image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:36.506534image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:40.379676image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:44.880358image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:48.275761image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:51.336215image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:54.646083image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:59.328073image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:02.808827image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:05.865935image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:08.987220image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:13.497901image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:17.437550image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:20.683214image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:24.209882image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:28.929761image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:36.689206image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:40.676209image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:45.071524image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:48.503040image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:51.546793image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:54.994909image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:59.537399image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:03.015732image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:06.080875image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:09.194457image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:13.843754image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:17.643586image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:20.888662image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:24.409277image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:29.196630image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:36.889851image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:41.015908image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:45.275397image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:48.703722image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:51.785031image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:55.313194image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:00.134017image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:03.241260image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:06.297926image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:09.522842image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:14.741048image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:17.851843image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:21.129624image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:24.700573image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:29.515171image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:37.105528image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:41.325820image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:45.495220image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:48.905563image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:51.975694image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:55.657256image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:00.329136image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:03.448606image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:06.504855image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:09.868963image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:14.954307image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:18.066974image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:21.328809image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:24.979821image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:29.781061image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:37.289903image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:41.661227image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:45.690011image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:49.091274image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:52.161898image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:55.974318image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:00.538864image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:03.659317image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:06.700988image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:10.154204image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:15.146178image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:18.255685image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:21.556292image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:25.310702image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:30.108767image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:37.779140image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:41.980216image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:45.884993image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:49.279427image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:52.352425image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:56.237257image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:00.727131image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:03.840608image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:06.890994image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:10.449407image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:15.338298image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:18.489270image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:21.751251image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:25.646769image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:30.329967image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:37.990490image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:42.320252image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:46.088708image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:49.528841image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:52.574235image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:56.584072image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:00.950005image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:04.032392image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:07.101278image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:10.775624image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:15.566994image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:18.700203image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:21.977862image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:25.954451image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:30.557300image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:38.177346image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:42.641304image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:46.279299image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:49.713853image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:52.793321image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:56.908702image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:01.174854image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:04.256077image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:07.306825image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:11.113262image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:15.763182image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:18.909614image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:22.181191image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:26.260328image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:30.779457image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:38.371951image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:43.005112image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:46.523280image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:49.946655image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:52.994414image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:57.181973image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:01.370010image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:04.459053image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:07.540720image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:11.404747image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:16.004482image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:19.142793image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:22.402030image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:26.579045image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:30.967738image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:38.571993image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:43.351047image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:46.717716image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:50.140743image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:53.190953image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:57.533713image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:01.596279image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:04.679346image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:07.736050image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:11.710999image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:16.204482image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:19.347615image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:22.778972image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:26.893337image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:31.158452image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:38.835521image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:43.701149image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:46.928360image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:50.332586image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:53.380451image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:35:57.850500image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:01.788147image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:04.880349image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:07.952034image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:11.963449image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:16.406026image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:19.589210image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:22.997216image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-02T16:36:27.189307image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-11-02T16:36:52.800296image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
AvailabilityCostsCustomer demographicsDefect ratesInspection resultsLead timeLead timesLocationManufacturing costsManufacturing lead timeNumber of products soldOrder quantitiesPriceProduct typeProduction volumesRevenue generatedRoutesShipping carriersShipping costsShipping timesStock levelsSupplier nameTransportation modes
Availability1.000-0.0420.1150.0290.158-0.1860.1690.0000.1420.0770.0680.1280.0000.0000.071-0.0770.1490.171-0.056-0.039-0.0480.1760.000
Costs-0.0421.0000.0810.0400.0000.0490.2460.091-0.025-0.094-0.0350.1530.0960.000-0.0780.0330.1170.2230.041-0.023-0.0070.0000.246
Customer demographics0.1150.0811.0000.0990.1400.0000.0000.0000.0000.0000.1470.2020.0000.1890.0000.2170.0000.0000.0000.0000.1310.0300.000
Defect rates0.0290.0400.0991.0000.0160.2800.0050.0000.0080.120-0.0860.006-0.1490.0000.117-0.1300.0000.0680.082-0.035-0.1530.0000.222
Inspection results0.1580.0000.1400.0161.0000.0000.0000.0000.0000.2570.1870.0000.0000.0000.0000.0000.0000.1580.0000.0000.0000.2790.000
Lead time-0.1860.0490.0000.2800.0001.0000.0050.000-0.1240.0150.046-0.0860.1550.1610.188-0.0100.0000.1040.008-0.0520.0800.0000.036
Lead times0.1690.2460.0000.0050.0000.0051.0000.090-0.020-0.011-0.0310.1190.0380.114-0.137-0.0690.2410.054-0.118-0.0280.0950.0000.036
Location0.0000.0910.0000.0000.0000.0000.0901.0000.1750.0000.1930.0340.1250.0000.0880.0000.0000.0000.0610.0000.0760.0000.063
Manufacturing costs0.142-0.0250.0000.0080.000-0.124-0.0200.1751.000-0.1370.035-0.035-0.1870.2480.079-0.2220.0000.0000.0190.0370.0260.1640.000
Manufacturing lead time0.077-0.0940.0000.1200.2570.015-0.0110.000-0.1371.000-0.0770.104-0.2910.0810.1950.0090.0000.000-0.0070.020-0.0650.0000.000
Number of products sold0.068-0.0350.147-0.0860.1870.046-0.0310.1930.035-0.0771.0000.0270.0070.1610.1670.0020.0000.0000.0500.0900.0560.1750.000
Order quantities0.1280.1530.2020.0060.000-0.0860.1190.034-0.0350.1040.0271.0000.1020.137-0.0640.0710.0860.000-0.0040.003-0.0920.0380.000
Price0.0000.0960.000-0.1490.0000.1550.0380.125-0.187-0.2910.0070.1021.0000.260-0.1170.0320.0890.0000.0450.0750.0930.0000.108
Product type0.0000.0000.1890.0000.0000.1610.1140.0000.2480.0810.1610.1370.2601.0000.0000.0000.0000.1070.1550.0000.1180.1990.000
Production volumes0.071-0.0780.0000.1170.0000.188-0.1370.0880.0790.1950.167-0.064-0.1170.0001.000-0.0310.1380.000-0.096-0.0640.0290.1550.000
Revenue generated-0.0770.0330.217-0.1300.000-0.010-0.0690.000-0.2220.0090.0020.0710.0320.000-0.0311.0000.0000.000-0.092-0.125-0.1250.0660.000
Routes0.1490.1170.0000.0000.0000.0000.2410.0000.0000.0000.0000.0860.0890.0000.1380.0001.0000.0000.0000.0810.0000.0000.000
Shipping carriers0.1710.2230.0000.0680.1580.1040.0540.0000.0000.0000.0000.0000.0000.1070.0000.0000.0001.0000.0000.1420.2160.0000.145
Shipping costs-0.0560.0410.0000.0820.0000.008-0.1180.0610.019-0.0070.050-0.0040.0450.155-0.096-0.0920.0000.0001.0000.0620.0820.0000.000
Shipping times-0.039-0.0230.000-0.0350.000-0.052-0.0280.0000.0370.0200.0900.0030.0750.000-0.064-0.1250.0810.1420.0621.000-0.1160.0000.157
Stock levels-0.048-0.0070.131-0.1530.0000.0800.0950.0760.026-0.0650.056-0.0920.0930.1180.029-0.1250.0000.2160.082-0.1161.0000.0000.000
Supplier name0.1760.0000.0300.0000.2790.0000.0000.0000.1640.0000.1750.0380.0000.1990.1550.0660.0000.0000.0000.0000.0001.0000.205
Transportation modes0.0000.2460.0000.2220.0000.0360.0360.0630.0000.0000.0000.0000.1080.0000.0000.0000.0000.1450.0000.1570.0000.2051.000

Missing values

2024-11-02T16:36:31.506921image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-02T16:36:32.218133image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Product typeSKUPriceAvailabilityNumber of products soldRevenue generatedCustomer demographicsStock levelsLead timesOrder quantitiesShipping timesShipping carriersShipping costsSupplier nameLocationLead timeProduction volumesManufacturing lead timeManufacturing costsInspection resultsDefect ratesTransportation modesRoutesCosts
0haircareSKU069.808006558028661.996792Non-binary587964Carrier B2.956572Supplier 3Mumbai292152946.279879Pending0.226410RoadRoute B187.752075
1skincareSKU114.843523957367460.900065Female5330372Carrier A9.716575Supplier 3Mumbai235173033.616769Pending4.854068RoadRoute B503.065579
2haircareSKU211.3196833489577.749626Unknown110882Carrier B8.054479Supplier 1Mumbai129712730.688019Pending4.580593AirRoute C141.920282
3skincareSKU361.16334368837766.836426Non-binary2313596Carrier C1.729569Supplier 5Kolkata249371835.624741Fail4.746649RailRoute A254.776159
4skincareSKU44.805496268712686.505152Non-binary53568Carrier A3.890548Supplier 1Delhi5414392.065161Fail3.145580AirRoute A923.440632
5haircareSKU51.699976871472828.348746Non-binary9027663Carrier B4.444099Supplier 4Bangalore101041756.766476Fail2.779194RoadRoute A235.461237
6skincareSKU64.07833348657823.476560Male1115588Carrier C3.880763Supplier 3Kolkata14314241.085069Pending1.000911SeaRoute A134.369097
7cosmeticsSKU742.958384594268496.103813Female9317111Carrier B2.348339Supplier 4Bangalore22564199.466109Fail0.398177RoadRoute C802.056312
8cosmeticsSKU868.717597781507517.363211Female510157Carrier C3.404734Supplier 4Mumbai13769811.423027Pending2.709863SeaRoute B505.557134
9skincareSKU964.015733359804971.145988Unknown1427831Carrier A7.166645Supplier 2Chennai299632347.957602Pending3.844614RailRoute B995.929461
Product typeSKUPriceAvailabilityNumber of products soldRevenue generatedCustomer demographicsStock levelsLead timesOrder quantitiesShipping timesShipping carriersShipping costsSupplier nameLocationLead timeProduction volumesManufacturing lead timeManufacturing costsInspection resultsDefect ratesTransportation modesRoutesCosts
90skincareSKU9013.881914563209592.633570Non-binary6618967Carrier B7.674431Supplier 3Bangalore8585885.675963Pass1.219382RailRoute B990.078473
91cosmeticsSKU9162.111965909161935.206794Male9822857Carrier B7.471514Supplier 4Delhi52072839.772883Pending0.626002RailRoute B996.778315
92cosmeticsSKU9247.714233442762100.129755Male9025108Carrier B4.469500Supplier 2Mumbai46712962.612690Pass0.333432RailRoute B230.092783
93haircareSKU9369.290831881144531.402134Unknown6317661Carrier C7.006432Supplier 4Chennai218242035.633652Fail4.165782AirRoute A823.523846
94cosmeticsSKU943.037689979877888.356547Unknown7726729Carrier B6.942946Supplier 2Delhi129081460.387379Pass1.463607RailRoute B846.665257
95haircareSKU9577.903927656727386.363944Unknown1514269Carrier B8.630339Supplier 4Mumbai184502658.890686Pending1.210882AirRoute A778.864241
96cosmeticsSKU9624.423131293247698.424766Non-binary672323Carrier C5.352878Supplier 3Mumbai286482817.803756Pending3.872048RoadRoute A188.742141
97haircareSKU973.52611156624370.916580Male461949Carrier A7.904846Supplier 4Mumbai105351365.765156Fail3.376238RoadRoute A540.132423
98skincareSKU9819.754605439138525.952560Female531277Carrier B1.409801Supplier 5Chennai2858195.604691Pending2.908122RailRoute A882.198864
99haircareSKU9968.517833176279185.185829Unknown558596Carrier B1.311024Supplier 2Chennai29921238.072899Fail0.346027RailRoute B210.743009